Analysis performed on enero 16, 2023 at 20:24:21, using CamPype


Sequencing summary before and after quality control (QC)

Before QC

Following tables show, by sample, the number, mean length and length standard deviation of R1 and R2 reads.

Select the parameter

Number of reads R1
Average read length R1
Number of reads R2
Average read length R2

After QC

QC was performed with Trimmomatic and PRINSEQ.

Following tables show, by sample, the number, mean length and length standard deviation of R1 and R2 reads.

Select the parameter

Number of reads R1
Average read length R1
Number of reads R2
Average read length R2


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Joined reads summary

Following table shows the number of reads, average length and standard deviation for joined and unjoined reads using FLASH.

Select the parameter

Percentage of joined reads
Number of joined reads
Average joined reads length
Number of unjoined R1 reads
Average unjoined R1 reads length
Number of unjoined R2 reads
Average unjoined R2 reads length


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Multi-Locus Sequence Typing (MLST)


Following table shows the Sequence Type (ST), MLST allelic profile and Clonal Complex (CC) for each isolate using mlst and the PubMLST database.

Below each column name you will find a filter box that you can use to filter the table by columns. You can also filter by more than one column and export this new subset table into a separated file (see the export buttons available).


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Assembly analysis and annotation


Following table shows the number of contigs (> 200 bp), genome length, average contig length, N50, GC content (%) and depth coverage using SPAdes and annotation information using Prokka for each draft genome.

Below each column name you will find a filter box that you can use to filter the table by columns. You can also filter by more than one column and export this new subset table into a separated file (see the export buttons available).

Select the parameter

Number of contigs
Draft genome length
Average contig length
N50
GC Content
Number of CDS
Hypothetical proteins
Number of CRISPRs
Number of rRNAs
Number of tRNAs


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Antibiotic resistance genes


Following table shows antibiotic resistance genes by screening the draft genomes against the Pathogen Detection Reference Gene Catalog by using AMRFinderPlus. Any hit with coverage below 80 % and identity below 60 % was removed.

Below each column name you will find a filter box that you can use to filter the table by columns. You can also filter by more than one column and export this new subset table into a separated file (see the export buttons available).


Following figure represents presence/absence of antibiotic resistance genes in each draft genome against the Pathogen Detection Reference Gene Catalog by using AMRFinderPlus. Presence is represented as minimum coverage of 80 % and minimum identity of 60 %.


Point mutations conferring antibiotic resistance


Following table shows known point mutations by screening the draft genomes against the Pathogen Detection Reference Gene Catalog by using AMRFinderPlus. Any hit with coverage below 80 % and identity below 60 % was removed.

Below each column name you will find a filter box that you can use to filter the table by columns. You can also filter by more than one column and export this new subset table into a separated file (see the export buttons available).


Following figure represents known point mutations in each draft genome by using AMRFinderPlus. Presence is represented as minimum coverage of 80 % and minimum identity of 60 %.

Following table(s) show(s) antibiotic resistance genes by screening the draft genomes against the selected database(s) by using ABRicate. Any hit with coverage below 80 % and identity below 60 % was removed.

Below each column name you will find a filter box that you can use to filter the table by columns. You can also filter by more than one column and export this new subset table into a separated file (see the export buttons available).

Select the database

argannot
CARD
Megares
NCBI
Resfinder



Following figure(s) represent(s) presence/absence of antibiotic resistance genes in each draft genome against the selected database(s) by using ABRicate. Presence is represented as minimum coverage of 80 % and minimum identity of 60 %.

Select the database

argannot

card

megares

ncbi

resfinder



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Virulence Genes


Following table(s) show(s) virulence genes by screening the draft genomes against the selected database(s) by using ABRicate. Any hit with coverage below 80 % and identity below % was removed.

Below each column name you will find a filter box that you can use to filter the table by columns. You can also filter by more than one column and export this new subset table into a separated file (see the export buttons available).

Select the database

VFDB



Following figure represents presence/absence of virulence genes in each draft genome against the selected database(s) by using ABRicate. Presence is represented as minimum coverage of 80 % and minimum identity of 60 %.


Following table shows virulence genes by screening the draft genomes against the inhouse VFDB database using BLAST. Any hit with identity below 50 % was removed.

Below each column name you will find a filter box that you can use to filter the table by columns. You can also filter by more than one column and export this new subset table into a separated file (see the export buttons available).


Following figure represents presence/absence of virulence genes in each draft genome against the inhouse VFDB database using BLAST. Presence is represented as minimum coverage of 80 % and minimum identity of 60 %.


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Plasmids


No plasmids found in any of the draft genomes.


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Pangenome analysis


Following charts show pangenome analysis with minimum 95 % identity for blastp using Roary.




Binary heatmap shows the presence (dark grey) and absence (white) of genes. Phylogeny for each isolate is shown on the left and was constructed based on accesory genes from the pangenome.

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Genomic variants


Following table shows genomic variants in each draft genome against the reference genome you indicated using Snippy.

Below each column name you will find a filter box that you can use to filter the table by columns. You can also filter by more than one column and export this new subset table into a separated file (see the export buttons available).


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Summary report


Following table shows summary information for each draft genome.

Below each column name you will find a filter box that you can use to filter the table by columns. You can also filter by more than one column and export this new subset table into a separated file (see the export buttons available).


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How to cite


Irene Ortega-Sanz, Jose A. Barbero and Antonio Canepa. CamPype (2022). Available at https://github.com/JoseBarbero/CamPype

Following packages and tools were used in CamPype:

Package/Tool Reference
Trimmomatic A.M. Bolger et al., 2014
Prinseq R. Schmieder and R. Edwards, 2011
FLASH T. Magoc and S. Salzberg, 2011
SPAdes A. Bankevich et al., 2012
QUAST A. Gurevich et al., 2013
progressiveMauve A.E. Darling et al., 2010
mlst T. Seemann
Abricate T. Seemann
BLAST Z. Zhang et al., 2000
AMRFinderPlus M. Feldgarden et al., 2019
Prokka T. Seemann, 2014
DFAST Y. Tanizawa et al., 2018
Roary A.J. Page et al., 2015
snippy T. Seemann
ape Paradis and Schliep, 2019
complexHeatmap Gu et al., 2016
dplyr Wickham et al., 2022
DT Xie et al., 2022
ggplot2 Wickham, 2016
ggtree Yu et al., 2017
pander Daróczi and Tsegelskyi, 2022
phytools Revell, 2012
plotly Sievert, 2020
rjson Couture-Beil, 2022
rmarkdown Allaire et al., 2022
tidyverse Wickham et al., 2019

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